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Foreword (partial) "Quantitative analysis, when it was first introduced, showed great promise for improving the performance of active equity managers. Traditional, fundamentally based managers had a long history of underperforming and charging high fees for doing so. A 1940 best-seller, Where Are the Customers’ Yachts? by Fred Schwed, Jr., prefigured the performance measurement revolution of the 1960s and 1970s by pointing out that, although Wall Street tycoons were earning enough profit that they could buy yachts, their customers were not getting much for their money. With few benchmarks and little performance measurement technology, it was difficult to make this charge stick. But after William Sharpe showed the world in 1963 how to calculate alpha and beta, and argued that only a positive alpha is worth an active management fee, underperformance by active equity managers became a serious issue, and a performance race was on. A key group of participants in this performance race were quantitative analysts, known as “quants.” Quants, by and large, rejected fundamental analysis of securities in favor of statistical techniques aimed at identifying common factors in security returns. These quants emerged, mostly out of academia, during the generation following Sharpe’s seminal work on the market model (see his 1963 paper in Note 2) and the capital asset pricing model (CAPM).3 Because these models implied that any systematic beat-the-market technique would not work (the expected value of alpha in the CAPM being zero), fame and fortune would obviously accrue to anyone who could find an apparent violation of the CAPM’s conclusions, or an “anomaly.” Thus, armies of young professors set about trying to do just that. During the 1970s and 1980s, several thousand papers were published in which anomalies were proposed and tested. This flood of effort constituted what was almost certainly the greatest academic output on a single topic in the history of finance. Quantitative equity management grew out of the work of these researchers and brought practitioners and academics together in the search for stock factors and characteristics that would beat the market on a risk-adjusted basis. With its emphasis on benchmarking, management of tracking error, mass production of investment insights by using computers to analyze financial data, attention to costs, and respect for finance theory, quant management promised to streamline and improve the investment process."

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